Everyone's building MCP servers for network automation. Your agents can finally talk to each other and share context about your infrastructure. But what context are they actually sharing?

If your agent's understanding of the network comes from vector embeddings and RAG, MCP is just helping you share incomplete topology understanding and missed policy dependencies faster. Vector similarity can't represent "which devices are upstream of this link" or "what routing policies affect this prefix."

MCP makes context sharing easy. Knowledge graphs make that context actually correct.

This talk will discuss lessons learned as a developer advocate maintaining coffeeAGNTCY, an open-source multi-agent system. Mainly, sharing the discovery of why knowledge graphs with LangGraph are essential for network automation agents.

We'll cover:

-What MCP servers can't fix (the context representation problem) -Knowledge graphs for network topology, routing, and policy dependencies -LangGraph for reasoning over graph-structured network data -Real patterns from coffeeAGNTCY project (lungo) . Code: https://github.com/agntcy/coffeeAgntcy/tree/main/coffeeAGNTCY/coffee_agents/lungo